Difference between HADOOP vs RDBMS
Hadoop software framework work is very well structured semi-structured and unstructured data. This also supports a variety of data formats in real-time such as XML, JSON, and text-based flat file formats. RDBMS works efficiently when there is an entity-relationship flow that is defined perfectly and therefore, the database schema or structure can grow and unmanaged otherwise. i.e., An RDBMS works well with structured data. Hadoop will be a good choice in environments when there are needs for big data processing on which the data being processed does not have dependable relationships.
What is Hadoop?
Hadoop is fundamentally an open-source infrastructure software framework that allows distributed storage and processing a huge amount of data i.e. Big Data. It’s a cluster system which works as a Master-Slave Architecture. Hence, with such architecture, large data can be stored and processed in parallel. Different types of data can be analyzed, structured(tables), unstructured (logs, email body, blog text) and semi-structured (media file metadata, XML, HTML).
Components of Hadoop
- HDFS: Hadoop Distributed File System. Google published its paper GFS and based on that HDFS was developed. It states that the files will be broken into blocks and stored in nodes over the distributed architecture. Doug Cutting and Yahoo! reverse engineered the model GFS and built a parallel Hadoop Distributed File System (HDFS)
- Yarn: Yet another Resource Negotiator is used for job scheduling and manages the cluster. It was introduced in Hadoop 2.
- Map Reduce: This is a framework that helps Java programs to do the parallel computation on data using a key-value pair. The Map takes input data and converts it into a data set which can be computed in Key value pair. The output of Map is consumed by reduce task and then the out of reducer gives the desired result.
- Hadoop Common: These Java libraries are used to start Hadoop and are used by other Hadoop modules.
What is RDBMS?
RDBMS stands for the relational database management system. It is a database system based on the relational model specified by Edgar F. Codd in 1970. The database management software like Oracle server, My SQL, and IBM DB2 are based on the relational database management system.
The data represented in the RDBMS is in the form of the rows or the tuples. This table is basically a collection of related data objects and it consists of columns and rows. Normalization plays a crucial role in RDBMS. It contains the group of the tables, each table contains the primary key.
Components of RDBMS
In RDBMS, a table is a record that is stored as vertically plus horizontally grid form. It is comprised of a set of fields, such as the name, address, and product of the data.
The rows in each table represent horizontal values.
Columns in a table are stored horizontally, each column represents a field of data.
They are identification tags for each row of data.
Hadoop and RDBMS have different concepts for storing, processing and retrieving the data/information. Hadoop is new in the market but RDBMS is approx. 50 years old. As time passes, data is growing in an exponential curve as well as the growing demands of data analysis and reporting.
Storing and processing with this huge amount of data within a rational amount of time becomes vital in current industries. RDBMS is more suitable for relational data as it works on tables. The main feature of the relational database includes the ability to use tables for data storage while maintaining and enforcing certain data relationships.
Below is the Infographics Between HADOOP vs RDBMS
Key Difference between HADOOP vs RDBMS
An RDBMS works well with structured data. Hadoop will be a good choice in environments when there are needs for big data processing on which the data being processed does not have dependable relationships. When a size of data is too big for complex processing and storing or not easy to define the relationships between the data, then it becomes difficult to save the extracted information in an RDBMS with a coherent relationship. Hadoop software framework work is very well structured semi-structured and unstructured data. RDBMS database technology is a very proven, consistent, matured and highly supported by world best companies. It works well with data descriptions such as data types, relationships among the data, constraints, etc. Hence, this is more appropriate for online transaction processing (OLTP).
What will be the future of RDBMS compares to Bigdata and Hadoop? Do you think RDBMS will be abolished anytime soon?
“There’s no relationship between the RDBMS and Hadoop right now — they are going to be complementary. It’s NOT about rip and replaces: we’re not going to get rid of RDBMS or MPP, but instead use the right tool for the right job — and that will very much be driven by price.”- Alisdair Anderson said at a Hadoop Summit.
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Head To Head Comparison Between HADOOP vs RDBMS
|Data Variety||Mainly for Structured data.||Used for Structured, Semi-Structured and Unstructured data|
|Data Storage||Average size data (GBS)||Use for large data set (Tbs and Pbs)|
|Querying||SQL Language||HQL (Hive Query Language)|
|Schema||Required on write (static schema)||Required on reading (dynamic schema)|
|Speed||Reads are fast||Both reads and writes are fast|
|Use Case||OLTP (Online transaction processing)||Analytics (Audio, video, logs etc), Data Discovery|
|Data Objects||Works on Relational Tables||Works on Key/Value Pair|
|Hardware Profile||High-End Servers||Commodity/Utility Hardware|
Conclusion – HADOOP vs RDBMS
By the above comparison, we have come to know that HADOOP is the best technique for handling Big Data compared to that of RDBMS. As day by day, the data used increases and therefore a better way of handling such a huge amount of data is becoming a hectic task. Analysis and storage of Big Data are convenient only with the help of the Hadoop eco-system than the traditional RDBMS. Hadoop is a large-scale, open-source software framework dedicated to scalable, distributed, data-intensive computing. This framework breakdowns large data into smaller parallelizable data sets and handles scheduling, maps each part to an intermediate value, Fault-tolerant, reliable, and supports thousands of nodes and petabytes of data, currently used in the development, production and testing environment and implementation options.